Correlation between nitrous oxide (N2O) emission and carbon to nitrogen (COD/N) ratio in denitrification process: a mitigation strategy to decrease greenhouse gas emission and cost of operation

Dynamically predicting nitrous oxide emissions in a full-scale industrial activated sludge reactor under multiple aeration patterns and COD/N ratios

The use of digital tools has become essential for quantifying and predicting greenhouse gas (GHG) emissions in urban wastewater treatment plants (WWTPs), enabling the development of operational regimes with a high probability of achieving net-zero targets. However, comprehensive studies documenting validation of model predictions-such as effluent quality, process economics, and emission factors-remain scarce within full-scale industrial settings. This paper aims to develop a decision support tool (DST) for (dynamically) predicting nitrous oxide (N2O) emissions in full-scale industrial activated sludge reactors (ASRs) and suggesting mitigation strategies. The DST, incorporating both biological and physico-chemical processes, was developed to address the unique characteristics of industrial wastewater. Specialized Gas-Liquid (G-L) mass transfer routines were also formulated to account for alternating anoxic and aerobic conditions in covered reactors. The proposed approach was validated using full-scale data collected at varying frequencies (from daily to minute intervals) during different campaigns at the largest industrial wastewater treatment system in Northern Europe. The DST was further tested across multiple aeration patterns and influent COD/N ratios. Results show that DST simulations can reproduce (daily) biological COD and nitrogen removal, sulfur transformations, and the physico-chemical precipitation of phosphorus with aluminum, achieving a deviation of 8.6 % over a six-week period. High-frequency (minute-level) dynamics for multiple nitrogen species (NHx, NO2-, NO3-, dissolved and gaseous N2O), dissolved oxygen (DO), and airflow were captured with a NRMSE of 0.16, 0.14 and 0.11 for three evaluated operational strategies (Baseline, Scenario #1 and #2), respectively. Both plant data and DST predictions indicate that the correlation (R2 up to 0.9) between emission factors (EFs) and influent COD/N ratios is significantly influenced by: i) oxygen supply dynamics (fast/slow) and ii) the duration of aeration periods. These EFs range from 0.2 % to 1.4 %. Analysis of derivatives identifies the denitrification (DEN) pathway as the primary contributor to N2O production, peaking at the anoxic phases, with the nitrifier-denitrification (ND) pathway contributing to a lesser extent at the end of aeration. Additionally, the DST generated response surfaces illustrating the key performance indicator (KPI) variations in EFs, nitrification capacity, effluent quality, and aeration energy consumption as functions of different aeration setpoints (DO and NO2-) across varying COD/N loads. The DST provided optimized strategies targeting those KPIs, which were successfully applied on site with improvements of most of the KPIs, achieving up to 71 % reductions of N2O emission (1.4 % to 0.4 %), potentially mitigating >15,000 tons CO2-e per year. These results demonstrate the DST's potential for broader applications in wastewater treatment processes.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD - chemical oxygen demand, NH4-N - ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

Enzymatic regulation of N2O production by denitrifying bacteria in the sludge of biological nitrogen removal process

This study analyzed the activities of all denitrifying enzymes involved in the denitrification process under different organic loads in a continuously operating sequencing batch reactor (SBR), to reveal how the denitrifying enzymes performed while the denitrifying bacteria facing changes in organic load, and leading to nitrous oxide (N₂O) production by fine-tuning enzyme activities. Results show that the activities of nitrate reductase (Nar), nitrite reductase (Nir), nitric oxide reductase (Nor) and nitrous oxide reductase (N₂OR) increased with the increase of organic loads, and the increase of the activity of different enzymes promoted by the organic load increase were as Nar > Nir > Nor > N₂OR. Compared with the Nar and Nir, the catalytic processes of the Nor and N₂OR were more susceptible to the influence of the substrate concentration and the content of internal and external carbon sources. The Nor usually maintained "excess" catalytic activity to ensure the smooth reduction of nitric oxide when the electron donor and substrate were sufficient. Otherwise, it reduced to a relatively lower catalytic activity and remained stable. The activities of the N₂OR were generally weaker than that of other denitrifying enzymes. More N₂O was produced in the period feeding with low organic loads (COD/NO₃^--N ≤ 4.9). The mechanism of the enzyme activities (Nor and N₂OR) regulating the total concentrations of N₂O was clarified. When the organic load was relatively low (COD/NO₃^--N ≤ 2.5), the N₂OR activity was inhibited due to its inability to acquire enough electrons, resulting the production of N₂O. When the organic load was moderate (2.5 < COD/NO₃^--N ≤ 4.9), the N₂OR activity was lower than the Nor activity due to the different activation rates of Nor and N₂OR by the substrate in bacteria, resulting the production of N₂O.

Reconstruction of corrupted datasets from ammonium-ISE sensors at WRRFs through merging with daily composite samples

Long-term, continuous datasets of high quality are important for instrumentation, control, and automation efforts of wastewater resources recovery facility (WRRFs). This study presents a methodology to increase the reliability of measurements from ammonium ion-selective electrodes (ISEs). This is done by correcting corrupted ISE data with a data source that often is available at WRRFs (volume-proportional composite samples). A yearlong measurement campaign showed that the existing standard protocols for sensor maintenance might still create corrupted dataset, with poor sensor recalibrations responsible for abrupt and unrealistic jumps in the measurements. The proposed automatic correction methodology removes both recalibration jumps and signal drift by using information from composite samples that already are taken for reporting to legal authorities. Results showed that the developed methodology provided a continuous, high-quality time series without the major data quality issues of the original signal. In fact, the signal was improved for 87% of days when a reference sample was available. The effect of correcting the data before use in a data-driven software sensor was also investigated. The corrected dataset led to noticeably smaller day-to-day variations in estimated NH₄⁺ loads, and to large improvements on both median estimates and prediction bounds. The long time series allowed for an investigation of how much training data that is required to fit a software sensor, which provides estimates that are representative for the entire study period. The results showed that 8 weeks of data allowed for a good median estimate, while 16 weeks are required for obtaining good 80% prediction bounds. Overall, the proposed method can increase the applicability of relatively cheaper ISE sensors for ICA application within WRRFs.

Enhancing Nitrate Removal from Waters with Low Organic Carbon Concentration Using a Bioelectrochemical System—A Pilot-Scale Study

Assessments of groundwater aquifers made around the world show that in many cases, nitrate concentrations exceed the safe drinking water threshold. This study assessed how bioelectrochemical systems could be used to enhance nitrate removal from waters with low organic carbon concentrations. A two-chamber microbial electrosynthesis cell (MES) was constructed and operated for 45 days with inoculum that was taken from a municipal wastewater treatment plant. A study showed that MES can be used to enhance nitrate removal efficiency from 3.66% day−1 in a control reactor to 8.54% day−1 in the MES reactor, if a cathode is able to act as an electron donor for autotrophic denitrifying bacteria or there is reducing oxygen in a cathodic chamber to favor denitrification. In the MES, greenhouse gas emissions were also lower compared to the control. Nitrous oxide average fluxes were −639.59 and −9.15 µg N m−2 h−1 for the MES and control, respectively, and the average carbon dioxide fluxes were −5.28 and 43.80 mg C m−2 h−1, respectively. The current density correlated significantly with the dissolved oxygen concentration, indicating that it is essential to keep the dissolved oxygen concentration in the cathode chamber as low as possible, not only to suppress oxygen’s inhibiting effect on denitrification but also to achieve better power efficiency.

Impact of the treatment of NH3 emissions from pig farms on greenhouse gas emissions. Quantitative assessment from the literature data

In order to limit ammonia (NH₃) emissions from pig farms, various air cleaning solutions are widely applied. However, the literature data report that these systems (chemical scrubbers, bioscrubbers and biofilters) can be both inefficient and promote nitrous oxide (N₂O) production. As air cleaning technologies should not contribute to secondary trace gases that may have a stronger environmental impact than the raw gas compounds themselves, the objective of this study was to quantify the effect of NH₃ treatment in pig farms on greenhouse gas (GHG) emissions. GHGs (carbon dioxide, methane and nitrous oxide) emitted at the outlet of three different cleaning systems ("chemical scrubber", "bioscrubber" and "bioscrubber + denitrification step") were assessed and compared with the emissions generated by the exhaust air with "no treatment". The calculations show that the chemical scrubber has no effect whereas biological treatments can increase GHG emissions. The use of bioscrubbers alone for NH₃ removal can remain acceptable provided that less than 3% of the NH₃ entering the apparatus is converted into N₂O. In such cases, a maximum increase of 1.9% in GHG emissions could be obtained. Conversely, the addition of a denitrification step to a bioscrubber must be avoided. Increases in overall GHG emissions of up to 25.8% were calculated but more significant increases could occur. With regard to GHG emissions, it is concluded that the use of a chemical scrubber is more suitable than a bioscrubber to treat exhaust air from pig farms.

Effects of partially saturated conditions on the metabolically active microbiome and on nitrogen removal in vertical subsurface flow constructed wetlands

Nitrogen dynamics and its association to metabolically active microbial populations were assessed in two vertical subsurface vertical flow (VF) wetlands treating urban wastewater. These VF wetlands were operated in parallel with unsaturated (UVF) and partially saturated (SVF) configurations. The SVF wetland exhibited almost 2-fold higher total nitrogen removal rate (5 g TN m^-2 d^-1) in relation to the UVF wetland (3 g TN m^-2 d^-1), as well as a low NO_x-N accumulation (1 mg L^-1 vs. 26 mg L^-1 in SVF and UVF wetland effluents, respectively). After 6 months of operation, ammonia oxidizing prokaryotes (AOP) and nitrite oxidizing bacteria (NOB) displayed an important role in both wetlands. Oxygen availability and ammonia limiting conditions promoted shifts on the metabolically active nitrifying community within 'nitrification aggregates' of wetland biofilms. Ammonia oxidizing archaea (AOA) and Nitrospira spp. overcame ammonia oxidizing bacteria (AOB) in the oxic layers of both wetlands. Microbial quantitative and diversity assessments revealed a positive correlation between Nitrobacter and AOA, whereas Nitrospira resulted negatively correlated with Nitrobacter and AOB populations. The denitrifying gene expression was enhanced mainly in the bottom layer of the SVF wetland, in concomitance with the depletion of NO_x-N from wastewater. Functional gene expression of nitrifying and denitrifying populations combined with the active microbiome diversity brought new insights on the microbial nitrogen-cycling occurring within VF wetland biofilms under different operational conditions.